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Lessons from Biology: Genes, Neurons, Neocortex and the New Computing Model for Cognitive Information Technologies †

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Lessons from Biology: Genes, Neurons, Neocortex and the New Computing Model for Cognitive Information Technologies † Proceedings Lessons from Biology: Genes, Neurons, Neocortex and the New Computing Model for Cognitive † Information Technologies Rao Mikkilineni C3DNA, 7533 Kingsbury Ct, Cupertino, CA 95014, USA; [email protected]; Tel.: +1-408-406-7639 † Presented at the IS4SI 2017 Summit DIGITALISATION FOR A SUSTAINABLE SOCIETY, Gothenburg, Sweden, 12–16 June 2017. Published: 9 June 2017 Abstract: In this paper we analyze our current understanding of genes, neurons and the neocortex and draw a parallel to current implementations of cognitive computing in Silicon. We argue that current information technologies have evolved from the original stored program control architecture implementing the Turing machines which allowed us to model, configure, monitor and control any physical system using a Universal Turing Machine model. However, large scale of distributed computations and their tolerance requirement to fluctuations have created new challenges. We suggest that a new agent based computing model extension to the current Turing machine meets this challenge and provides a path to cognitive self-learning and self-managing systems. Keywords: genes; neurons; neocortex; cognition; DIME network architecture; self-managing computations; computing models 1. Introduction According to Ray Kurzweil [1] the story of human intelligence starts with a universe that is capable of encoding information. This was the enabling factor that allowed evolution to take place. The story of evolution, he observes, unfolds with increasing levels of abstraction that exploits information processing. From atoms to molecules to a very unique and complex molecule DNA, the increasing levels of abstraction of encoding information in reusable form and using it to define a process intent and delivering a means to execute it has resulted in a diversity of self-replicating system. The next major evolutionary abstractions, the neuron and the neural network allowed embedded, embodied, enacted and extended cognition (known as 4E cognition) in a variety of ways [2] to model, configure, monitor and control the environment using a set of information patterns with a self- identity and optimize the execution of the processes (both of managing its own life and of managing its interactions with the environment). Cognition [3] here is the ability to process information, apply knowledge, and change the circumstance. Cognition is associated with intent and its accomplishment through various information management processes that monitor and control a system and its environment. Cognition is associated with a sense of “self” (the observer) and the systems with which it interacts (the environment or the “observed”). Cognition extensively uses time and history in executing and regulating tasks that constitute a cognitive process. The Neocortex takes the neural network encoding of information to a higher level by creating a hierarchical temporal memory that not only composes complex information processing workflows that compose downstream specialized networks using neural processing units but also creates predictive and corrective patterns in real time. The neocortex holds the key to the learning processes which provides its cognitive data analytics and their predictive capabilities. In this paper we examine the evolution of Genes, Neurons and the Neocortex and the evolution of information processing in Silicon based computing systems. Prof. Mark Burgin [4] from UCLA has Proceedings 2017, 1, 213; doi:10.3390/IS4SI-2017-03989 www.mdpi.com/journal/proceedings Proceedings 2017, 1, 213 2 of 6 argued that the first mathematical model employing agent technology (called an inductive Turing machine) serves as a theoretical foundation for a new model, the DIME network architecture (DNA) and allows modelling a network as an extremely large dynamic memory. The DNA agents organize connections of an information processing network in Silicon to provide cognitive computing abstractions and manage the evolution of the computing network behavior. 2. Genes As George Dyson [5], in his book ‘Darwin among the Machines’, observes “The analog of software in the living world is not a self-reproducing organism, but a self-replicating molecule of DNA. Self-replication and self-reproduction have often been confused. Biological organisms, even single-celled organisms, do not replicate themselves; they host the replication of genetic sequences that assist in reproducing an approximate likeness of them. For all but the lowest organisms, there is a lengthy, recursive sequence of nested programs to unfold. An elaborate self-extracting process restores entire directories of compressed genetic programs and reconstructs increasingly complicated levels of hardware on which the operating system runs.” Life, it seems, is an executable directed acyclic graph (DAG) and a managed one at that. According to Richard Dawkins [6], DNA molecule is really a set of plans for building a body and it indirectly supervises the manufacture of a different kind of molecule—protein. “The coded message of the DNA, written in the four-letter nucleotide alphabet, is translated in a simple mechanical way into another alphabet. This is the alphabet of amino acids which spells out protein molecules.” The indirect supervision task mentioned by Dawkins is related to the function of the Gene, which is a piece of DNA material that contains all the information needed to “build” specific biological structure. According to Damasio [7], managing and safe keeping life is the fundamental premise of biological entities. At the base of these features there is the homeostasis process. Homeostasis is the property of a system that regulates its internal environment and tends to maintain a stable, constant condition of properties like temperature or chemical parameters that are essential to its survival. System-wide homeostasis goals are accomplished through a representation of current state, desired state, a comparison process and control mechanisms. Consciousness plays an important role in all these processes; the knowledge about the internal (self) and external (environment) conditions is the key element for understanding which action (or sequence of actions) has to be performed to reach a specific goal given the current situations. This brings to light the cellular computing model that: 1. Spells out the computational workflow components as a stable sequence of information and information processing patterns in executable form that accomplishes a specific purpose or intent, 2. Implements a parallel management workflow with another sequence of information patterns that assures the successful execution of the system’s purpose (the computing network to assure biological value with management and safekeeping). 3. Uses a signaling mechanism that controls the execution of the workflow for gene expression (the regulatory, i.e., management, network) and 4. Assures real-time monitoring and control (homeostasis) to execute the four genetic transactions of replication, repair, recombination and reconfiguration. 3. Neurons Neurons are special purpose cells created by genes that process information received through various sensor cells and control the environment using myriad motor cells. The neuron is the fundamental unit of the nervous system. The basic purpose of a neuron is to receive incoming information and, based upon that information, send a signal to other neurons, muscles, or glands. Neurons are designed to rapidly send signals across physiologically long distances. A group of neurons thus are programmed to perform specific functions (sensory and motor) that help an Proceedings 2017, 1, 213 3 of 6 organism to process information, detect patterns and take action based on signals received from within or without. The Hebbian theory of learning summarized as “cells that fire together wire together” has been the premise used to understand “neural nets” and neuronal learning. While Hebbian unit of learning is a neuron, Ray Kurtzweil on the other hand proposes an assembly (Lego-like) of neurons whose function and structure are predetermined by genes as a fundamental unit of learning executing information processing, pattern detection and communication with other units of learning. He goes on to say that the purpose of these modules is to recognize patterns, to remember them, and to predict them based on partial patterns. In essence, groups of neurons act as basic autonomic computing units with sensors and actuators processing information based on genetically programmed pattern recognition mechanisms. 4. Neocortex The neocortex plays a crucial role in the control of higher perceptual processes, cognitive functions, and intelligent behavior. It acts as higher level information processing mechanism that uses re-composable neural subnetworks to create a hierarchical information processing system with predictive and proactive analytics. In other words, it allows us to learn, adapt and create new modes of behavior. Ray Kurzweil [1] succinctly summarizes the nature and function of the neocortex. The basic unit of the neocortex is a module of neurons which he estimates at around a hundred. The pattern of connections and synaptic strengths within each module is relatively stable. He emphasizes that it is the connections and the synaptic strengths between the modules that represent learning. The physical connections between these modules are created to represent hierarchical information pattern processing workflows in a repeated and orderly manner. He asserts that the real strength of
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